Open AccessClassification of Sentiment on Business Data for Decision Making using Supervised Machine Learning Methods
Author(s) -
Siji George C G,
B. Sumathi
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6086.029320
Subject(s) - sentiment analysis , computer science , naive bayes classifier , decision tree , random forest , artificial intelligence , machine learning , support vector machine , categorization , product (mathematics) , data mining , set (abstract data type) , process (computing) , natural language processing , mathematics , geometry , programming language , operating system
Sentiment analysis is deals with the classification of sentiments expressed in a particular document. The analysis of user generated data by using sentiment analysis is very useful for knowing the opinion of a crowd. This paper is mainly aimed to tackle the problem of polarity categorization of sentiment analysis. A Detailed description of the sentiment analysis process is also given. Product review data set from UCI repository is used for analysis. This paper is giving a comparative analysis of four supervised machine learning algorithms namely Naive Bayes, Support Vector Machine, Decision Tree and Random Forest which are used for product review analysis. The result shows that, Random Forest classification algorithm provides better accuracy than other three algorithms